A New Scheme for Trajectory Visualization. - Yuejun Guo 001; Qing Xu 002; Xiu Li 005; Xiaoxiao Luo; Mateu Sbert (IV, 2014)

A mixed noise removal algorithm based on the maximum entropy principle. - Shang Wu 002; Qing Xu 002; Jialang Li; Yuejun Guo 001 (ICME, 2015)

XaIBO: An Extension of aIB for Trajectory Clustering with Outlier. - Yuejun Guo 001; Qing Xu 002; Sheng Liang; Yang Fan; Mateu Sbert (ICONIP (2), 2015)

Trajectory Abstracting with Group-Based Signal Denoising. - Xiaoxiao Luo; Qing Xu 002; Yuejun Guo 001; Hao Wei; Yimin Lv (ICONIP (3), 2015)

Visualization on Agglomerative Information Bottleneck Based Trajectory Clustering. - Yang Fan; Qing Xu 002; Yuejun Guo 001; Sheng Liang (IV, 2015)

Multiscale Visualization of Trajectory Data. - Sheng Liang; Qing Xu 002; Yuejun Guo 001; Yang Fan (IV, 2015)

3D Visualization of Multiscale Video Key Frames. - Shihua Sun; Qing Xu 002; Yuejun Guo 001; Sheng Liang; Yang Fan (IV, 2015)

Fast Agglomerative Information Bottleneck Based Trajectory Clustering. - Yuejun Guo 001; Qing Xu 002; Yang Fan; Sheng Liang; Mateu Sbert (ICONIP (3), 2016)

Selecting Video Key Frames Based on Relative Entropy and the Extreme Studentized Deviate Test. - Yuejun Guo 001; Qing Xu 002; Shihua Sun; Xiaoxiao Luo; Mateu Sbert (Entropy, 2016)

The Abstraction for Trajectories with Different Numbers of Sampling Points. - Peng Li; Qing Xu 002; Hao Wei; Yuejun Guo 001; Xiaoxiao Luo; Mateu Sbert (ICONIP (6), 2017)

Trajectory Shape Analysis and Anomaly Detection Utilizing Information Theory Tools . - Yuejun Guo 001; Qing Xu 002; Peng Li; Mateu Sbert; Yu Yang (Entropy, 2017)

IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clustering. - Yuejun Guo 001; Qing Xu 002; Mateu Sbert (Entropy, 2018)

Random-valued impulse noise removal using adaptive ranked-ordered impulse detector. - Qing Xu 002; Yunhe Li; Yuejun Guo 001; Shang Wu 002; Mateu Sbert (J. Electronic Imaging, 2018)

A group-based signal filtering approach for trajectory abstraction and restoration. - Yuejun Guo 001; Qing Xu 002; Xiaoxiao Luo; Hao Wei; Hongjuan Bu; Mateu Sbert (Neural Comput. Appl., 2018)

Eye Movement-Based Analysis on Methodologies and Efficiency in the Process of Image Noise Evaluation. - Cheng Peng; Qing Xu 002; Yuejun Guo 001; Klaus Schoeffmann (ICANN (3), 2019)

Anomaly Detection Based on the Global-Local Anomaly Score for Trajectory Data. - Chengcheng Li; Qing Xu 002; Cheng Peng; Yuejun Guo 001 (ICONIP (5), 2019)

Detail-Preserving Trajectory Summarization Based on Segmentation and Group-Based Filtering. - Ting Wu; Qing Xu 002; Yunhe Li; Yuejun Guo 001; Klaus Schoeffmann (MMM (2), 2019)

SHNN-CAD+: An Improvement on SHNN-CAD for Adaptive Online Trajectory Anomaly Detection. - Yuejun Guo 001; Anton Bardera (Sensors, 2019)

Trajectory Anomaly Detection Based on the Mean Distance Deviation. - Xiaoyuan Hu; Qing Xu 002; Yuejun Guo 001 (ICONIP (4), 2020)

Advanced techniques in trajectory data analysis for anomaly detection and map construction. - Yuejun Guo 001 (2020)

Towards Exploring the Limitations of Active Learning: An Empirical Study. - Qiang Hu; Yuejun Guo 001; Maxime Cordy; Xiaofei Xie; Wei Ma; Mike Papadakis; Yves Le Traon (ASE, 2021)

MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles. - Yuejun Guo 001; Qiang Hu; Maxime Cordy; Michail Papadakis; Yves Le Traon (CoRR, 2021)

A scalable method to construct compact road networks from GPS trajectories. - Yuejun Guo 001; Anton Bardera; Marta Fort; Rodrigo I. Silveira (Int. J. Geogr. Inf. Sci., 2021)

Robust active learning: sample-efficient training of robust deep learning models. - Yuejun Guo 001; Qiang Hu; Maxime Cordy; Mike Papadakis; Yves Le Traon (CAIN, 2022)

Labeling-Free Comparison Testing of Deep Learning Models. - Yuejun Guo 001; Qiang Hu; Maxime Cordy; Xiaofei Xie; Mike Papadakis; Yves Le Traon (CoRR, 2022)

Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment. - Qiang Hu; Yuejun Guo 001; Maxime Cordy; Xiaofei Xie; Wei Ma; Mike Papadakis; Yves Le Traon (CoRR, 2022)

CodeS: A Distribution Shift Benchmark Dataset for Source Code Learning. - Qiang Hu; Yuejun Guo 001; Xiaofei Xie; Maxime Cordy; Lei Ma 003; Mike Papadakis; Yves Le Traon (CoRR, 2022)

Efficient Testing of Deep Neural Networks via Decision Boundary Analysis. - Qiang Hu; Yuejun Guo 001; Xiaofei Xie; Maxime Cordy; Lei Ma 003; Mike Papadakis; Yves Le Traon (CoRR, 2022)

Enhancing Code Classification by Mixup-Based Data Augmentation. - Zeming Dong; Qiang Hu; Yuejun Guo 001; Maxime Cordy; Mike Papadakis; Yves Le Traon; Jianjun Zhao 001 (CoRR, 2022)

Enhancing Mixup-Based Graph Learning for Language Processing via Hybrid Pooling. - Zeming Dong; Qiang Hu; Yuejun Guo 001; Maxime Cordy; Mike Papadakis; Yves Le Traon; Jianjun Zhao 001 (CoRR, 2022)

An Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement. - Qiang Hu; Yuejun Guo 001; Maxime Cordy; Xiaofei Xie; Lei Ma 003; Mike Papadakis; Yves Le Traon (ACM Trans. Softw. Eng. Methodol., 2022)

Towards Understanding Model Quantization for Reliable Deep Neural Network Deployment. - Qiang Hu; Yuejun Guo 001; Maxime Cordy; Xiaofei Xie; Wei Ma; Mike Papadakis; Yves Le Traon (CAIN, 2023)

An Empirical Study of the Imbalance Issue in Software Vulnerability Detection. - Yuejun Guo 001; Qiang Hu; Qiang Tang 001; Yves Le Traon (ESORICS (4), 2023)

An Investigation of Quality Issues in Vulnerability Detection Datasets. - Yuejun Guo 001; Seifeddine Bettaieb (EuroS&P Workshops, 2023)

Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation. - Qiang Hu; Yuejun Guo 001; Xiaofei Xie; Maxime Cordy; Mike Papadakis; Lei Ma 003; Yves Le Traon (ICSE, 2023)

CodeS: Towards Code Model Generalization Under Distribution Shift. - Qiang Hu; Yuejun Guo 001; Xiaofei Xie; Maxime Cordy; Mike Papadakis; Lei Ma 003; Yves Le Traon (ICSE (NIER), 2023)

MUTEN: Mutant-Based Ensembles for Boosting Gradient-Based Adversarial Attack. - Qiang Hu; Yuejun Guo 001; Maxime Cordy; Mike Papadakis; Yves Le Traon (ASE, 2023)

Boosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study. - Zeming Dong; Qiang Hu; Yuejun Guo 001; Zhenya Zhang; Jianjun Zhao 001 (QRS Companion, 2023)

MixCode: Enhancing Code Classification by Mixup-Based Data Augmentation. - Zeming Dong; Qiang Hu; Yuejun Guo 001; Maxime Cordy; Mike Papadakis; Zhenya Zhang; Yves Le Traon; Jianjun Zhao 001 (SANER, 2023)

Boosting Source Code Learning with Data Augmentation: An Empirical Study. - Zeming Dong; Qiang Hu; Yuejun Guo 001; Zhenya Zhang; Maxime Cordy; Mike Papadakis; Yves Le Traon; Jianjun Zhao 001 (CoRR, 2023)

CodeLens: An Interactive Tool for Visualizing Code Representations. - Yuejun Guo 001; Seifeddine Bettaieb; Qiang Hu; Yves Le Traon; Qiang Tang 001 (CoRR, 2023)

Evaluating the Robustness of Test Selection Methods for Deep Neural Networks. - Qiang Hu; Yuejun Guo 001; Xiaofei Xie; Maxime Cordy; Wei Ma; Mike Papadakis; Yves Le Traon (CoRR, 2023)

Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations. - Salah Ghamizi; Maxime Cordy; Yuejun Guo 001; Mike Papadakis; Yves Le Traon (CoRR, 2023)

An Empirical Study of Deep Learning-Based SS7 Attack Detection. - Yuejun Guo 001; Orhan Ermis; Qiang Tang 001; Hoang Trang; Alexandre De Oliveira (Inf., 2023)

DRE: density-based data selection with entropy for adversarial-robust deep learning models. - Yuejun Guo 001; Qiang Hu; Maxime Cordy; Michail Papadakis; Yves Le Traon (Neural Comput. Appl., 2023)

Poster: Automated Dependency Mapping for Web API Security Testing Using Large Language Models. - Wanpeng Li; Yuejun Guo 001 (CCS, 2024)

Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection. - Yuejun Guo 001; Constantinos Patsakis; Qiang Hu; Qiang Tang 001; Fran Casino (ESORICS (1), 2024)

Data Quality Issues in Vulnerability Detection Datasets. - Yuejun Guo 001; Seifeddine Bettaieb (CoRR, 2024)

AI-Driven Software Security: Vulnerability Detection, Patching, and Anti-Fuzzing. - Aayush Garg; Yuejun Guo 001; Qiang Tang 001 (ERCIM News, 2024)

On the effectiveness of hybrid pooling in mixup-based graph learning for language processing. - Zeming Dong; Qiang Hu; Zhenya Zhang; Yuejun Guo 001; Maxime Cordy; Mike Papadakis; Yves Le Traon; Jianjun Zhao 001 (J. Syst. Softw., 2024)

LaF: Labeling-free Model Selection for Automated Deep Neural Network Reusing. - Qiang Hu; Yuejun Guo 001; Xiaofei Xie; Maxime Cordy; Mike Papadakis; Yves Le Traon (ACM Trans. Softw. Eng. Methodol., 2024)

KAPE: kNN-based Performance Testing for Deep Code Search. - Yuejun Guo 001; Qiang Hu; Xiaofei Xie; Maxime Cordy; Mike Papadakis; Yves Le Traon (ACM Trans. Softw. Eng. Methodol., 2024)

Test Optimization in DNN Testing: A Survey. - Qiang Hu; Yuejun Guo 001; Xiaofei Xie; Maxime Cordy; Lei Ma 003; Mike Papadakis; Yves Le Traon (ACM Trans. Softw. Eng. Methodol., 2024)

Active Code Learning: Benchmarking Sample-Efficient Training of Code Models. - Qiang Hu; Yuejun Guo 001; Xiaofei Xie; Maxime Cordy; Lei Ma 003; Mike Papadakis; Yves Le Traon (IEEE Trans. Software Eng., 2024)

Towards Exploring the Limitations of Test Selection Techniques on Graph Neural Networks: An Empirical Study. - Xueqi Dang; Yinghua Li; Wei Ma; Yuejun Guo 001; Qiang Hu; Mike Papadakis; Maxime Cordy; Yves Le Traon (Empir. Softw. Eng., 2024)

A comprehensive analysis on software vulnerability detection datasets: trends, challenges, and road ahead. - Yuejun Guo 001; Seifeddine Bettaieb; Fran Casino (Int. J. Inf. Sec., 2024)

Evaluating Pre-Trained Models for Multi-Language Vulnerability Patching. - Zanis Ali Khan; Aayush Garg; Yuejun Guo 001; Qiang Tang 001 (CoRR, 2025)

Boosting source code learning with text-oriented data augmentation: an empirical study. - Zeming Dong; Qiang Hu; Yuejun Guo 001; Zhenya Zhang; Maxime Cordy; Mike Papadakis; Yves Le Traon; Jianjun Zhao 001 (Empir. Softw. Eng., 2025)

Assessing the Robustness of Test Selection Methods for Deep Neural Networks. - Qiang Hu; Yuejun Guo 001; Xiaofei Xie; Maxime Cordy; Wei Ma; Mike Papadakis; Lei Ma 003; Yves Le Traon (ACM Trans. Softw. Eng. Methodol., 2025)